Constructs symbolic derivatives of sum of `ys` w.r.t. x in `xs`.

`tf.gradients` is only valid in a graph context. In particular, it is valid in the context of a `tf.function` wrapper, where code is executing as a graph.

`ys` and `xs` are each a `Tensor` or a list of tensors. `grad_ys` is a list of `Tensor`, holding the gradients received by the `ys`. The list must be the same length as `ys`.

`gradients()` adds ops to the graph to output the derivatives of `ys` with respect to `xs`. It returns a list of `Tensor` of length `len(xs)` where each tensor is the `sum(dy/dx)` for y in `ys` and for x in `xs`.

`grad_ys` is a list of tensors of the same length as `ys` that holds the initial gradients for each y in `ys`. When `grad_ys` is None, we fill in a tensor of '1's of the shape of y for each y in `ys`. A user can provide their own initial `grad_ys` to compute the derivatives using a different initial gradient for each y (e.g., if one wanted to weight the gradient differently for each value in each y).

`stop_gradients` is a `Tensor` or a list of tensors to be considered constant with respect to all `xs`. These tensors will not be backpropagated through, as though they had been explicitly disconnected using `stop_gradient`. Among other things, this allows computation of partial derivatives as opposed to total derivatives. For example:

````@tf.function`
`def example():`
`  a = tf.constant(0.)`
`  b = 2 * a`
`  return tf.gradients(a + b, [a, b], stop_gradients=[a, b])`
`example()`
`[<tf.Tensor: shape=(), dtype=float32, numpy=1.0>,`
`<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]`
```

Here the partial derivatives `g` evaluate to `[1.0, 1.0]`, compared to the total derivatives `tf.gradients(a + b, [a, b])`, which take into account the influence of `a` on `b` and evaluate to `[3.0, 1.0]`. Note that the above is equivalent to:

````@tf.function`
`def example():`
`  a = tf.stop_gradient(tf.constant(0.))`
`  b = tf.stop_gradient(2 * a)`
`  return tf.gradients(a + b, [a, b])`
`example()`
`[<tf.Tensor: shape=(), dtype=float32, numpy=1.0>,`
`<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]`
```

`stop_gradients` provides a way of stopping gradient after the graph has already been constructed, as compared to `tf.stop_gradient` which is used during graph construction. When the two approaches are combined, backpropagation stops at both `tf.stop_gradient` nodes and nodes in `stop_gradients`, whichever is encountered first.

All integer tensors are considered constant with respect to all `xs`, as if they were included in `stop_gradients`.

`unconnected_gradients` determines the value returned for each x in xs if it is unconnected in the graph to ys. By default this is None to safeguard against errors. Mathematically these gradients are zero which can be requested using the `'zero'` option. `tf.UnconnectedGradients` provides the following options and behaviors:

````@tf.function`
`def example(use_zero):`
`  a = tf.ones([1, 2])`
`  b = tf.ones([3, 1])`
`  if use_zero:`
`    return tf.gradients([b], [a], unconnected_gradients='zero')`
`  else:`
`    return tf.gradients([b], [a], unconnected_gradients='none')`
`example(False)`
`[None]`
`example(True)`
`[<tf.Tensor: shape=(1, 2), dtype=float32, numpy=array([[0., 0.]], ...)>]`
```

Let us take one practical example which comes during the back propogation phase. This function is used to evaluate the derivatives of the cost function with respect to Weights `Ws` and Biases `bs`. Below sample implementation provides the exaplantion of what it is actually used for :

````@tf.function`
`def example():`
`  Ws = tf.constant(0.)`
`  bs = 2 * Ws`
`  cost = Ws + bs  # This is just an example. Please ignore the formulas.`
`  g = tf.gradients(cost, [Ws, bs])`
`  dCost_dW, dCost_db = g`
```